Overview

Dataset statistics

Number of variables12
Number of observations440833
Missing cells12
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory110.1 MiB
Average record size in memory262.0 B

Variable types

Numeric8
Categorical4

Alerts

Churn is highly overall correlated with CustomerID and 1 other fieldsHigh correlation
CustomerID is highly overall correlated with ChurnHigh correlation
Support Calls is highly overall correlated with ChurnHigh correlation
Support Calls has 69875 (15.9%) zerosZeros
Payment Delay has 16904 (3.8%) zerosZeros

Reproduction

Analysis started2025-12-16 17:54:06.886476
Analysis finished2025-12-16 17:54:39.870801
Duration32.98 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

CustomerID
Real number (ℝ)

High correlation 

Distinct440832
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean225398.67
Minimum2
Maximum449999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-12-16T23:24:40.035256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile22050.55
Q1113621.75
median226125.5
Q3337739.25
95-th percentile425905.45
Maximum449999
Range449997
Interquartile range (IQR)224117.5

Descriptive statistics

Standard deviation129531.92
Coefficient of variation (CV)0.57467917
Kurtosis-1.2006439
Mean225398.67
Median Absolute Deviation (MAD)112049.5
Skewness-0.018485823
Sum9.9362946 × 1010
Variance1.6778518 × 1010
MonotonicityStrictly increasing
2025-12-16T23:24:40.215284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
121
 
< 0.1%
Other values (440822)440822
> 99.9%
ValueCountFrequency (%)
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
111
< 0.1%
121
< 0.1%
ValueCountFrequency (%)
4499991
< 0.1%
4499981
< 0.1%
4499971
< 0.1%
4499961
< 0.1%
4499951
< 0.1%
4499941
< 0.1%
4499931
< 0.1%
4499921
< 0.1%
4499911
< 0.1%
4499901
< 0.1%

Age
Real number (ℝ)

Distinct48
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean39.373153
Minimum18
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-12-16T23:24:40.377296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q129
median39
Q348
95-th percentile61
Maximum65
Range47
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.442369
Coefficient of variation (CV)0.3160115
Kurtosis-0.86485336
Mean39.373153
Median Absolute Deviation (MAD)9
Skewness0.16201568
Sum17356946
Variance154.81256
MonotonicityNot monotonic
2025-12-16T23:24:40.539782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
5013527
 
3.1%
4212578
 
2.9%
4012417
 
2.8%
4812379
 
2.8%
4712369
 
2.8%
4612368
 
2.8%
4412344
 
2.8%
4912331
 
2.8%
4112314
 
2.8%
4312298
 
2.8%
Other values (38)315907
71.7%
ValueCountFrequency (%)
188219
1.9%
198073
1.8%
209553
2.2%
219574
2.2%
229639
2.2%
239513
2.2%
249465
2.1%
259647
2.2%
269692
2.2%
279472
2.1%
ValueCountFrequency (%)
655460
1.2%
645496
1.2%
635560
1.3%
625288
1.2%
615407
1.2%
605430
1.2%
595573
1.3%
585373
1.2%
575361
1.2%
565477
1.2%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size26.0 MiB
Male
250252 
Female
190580 

Length

Max length6
Median length4
Mean length4.8646378
Min length4

Characters and Unicode

Total characters2144488
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male250252
56.8%
Female190580
43.2%
(Missing)1
 
< 0.1%

Length

2025-12-16T23:24:40.690808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-16T23:24:40.776365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male250252
56.8%
female190580
43.2%

Most occurring characters

ValueCountFrequency (%)
e631412
29.4%
a440832
20.6%
l440832
20.6%
M250252
 
11.7%
F190580
 
8.9%
m190580
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)2144488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e631412
29.4%
a440832
20.6%
l440832
20.6%
M250252
 
11.7%
F190580
 
8.9%
m190580
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2144488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e631412
29.4%
a440832
20.6%
l440832
20.6%
M250252
 
11.7%
F190580
 
8.9%
m190580
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2144488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e631412
29.4%
a440832
20.6%
l440832
20.6%
M250252
 
11.7%
F190580
 
8.9%
m190580
 
8.9%

Tenure
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean31.256336
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-12-16T23:24:40.903809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q116
median32
Q346
95-th percentile58
Maximum60
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.255727
Coefficient of variation (CV)0.55207135
Kurtosis-1.192523
Mean31.256336
Median Absolute Deviation (MAD)15
Skewness-0.06140161
Sum13778793
Variance297.76013
MonotonicityNot monotonic
2025-12-16T23:24:41.134934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
327828
 
1.8%
497815
 
1.8%
567812
 
1.8%
557777
 
1.8%
337770
 
1.8%
527769
 
1.8%
307750
 
1.8%
477747
 
1.8%
487737
 
1.8%
577735
 
1.8%
Other values (50)363092
82.4%
ValueCountFrequency (%)
16407
1.5%
26575
1.5%
36417
1.5%
46606
1.5%
56669
1.5%
67704
1.7%
77569
1.7%
87670
1.7%
97534
1.7%
107674
1.7%
ValueCountFrequency (%)
607658
1.7%
597597
1.7%
587669
1.7%
577735
1.8%
567812
1.8%
557777
1.8%
547606
1.7%
537665
1.7%
527769
1.8%
517594
1.7%

Usage Frequency
Real number (ℝ)

Distinct30
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean15.807494
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-12-16T23:24:41.263730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median16
Q323
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.5862416
Coefficient of variation (CV)0.5431754
Kurtosis-1.1758148
Mean15.807494
Median Absolute Deviation (MAD)7
Skewness-0.043473478
Sum6968449
Variance73.723546
MonotonicityNot monotonic
2025-12-16T23:24:41.381539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1115311
 
3.5%
2915284
 
3.5%
2015258
 
3.5%
2515237
 
3.5%
3015232
 
3.5%
2115205
 
3.4%
1915204
 
3.4%
1215179
 
3.4%
2615134
 
3.4%
1515129
 
3.4%
Other values (20)288659
65.5%
ValueCountFrequency (%)
113797
3.1%
213633
3.1%
313843
3.1%
413549
3.1%
513716
3.1%
613746
3.1%
713555
3.1%
813725
3.1%
913770
3.1%
1015090
3.4%
ValueCountFrequency (%)
3015232
3.5%
2915284
3.5%
2815012
3.4%
2715121
3.4%
2615134
3.4%
2515237
3.5%
2415038
3.4%
2315072
3.4%
2215005
3.4%
2115205
3.4%

Support Calls
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean3.6044366
Minimum0
Maximum10
Zeros69875
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-12-16T23:24:41.493417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0702179
Coefficient of variation (CV)0.85178856
Kurtosis-0.74591196
Mean3.6044366
Median Absolute Deviation (MAD)2
Skewness0.66680851
Sum1588951
Variance9.4262378
MonotonicityNot monotonic
2025-12-16T23:24:41.593137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
069875
15.9%
169476
15.8%
266571
15.1%
352729
12.0%
438750
8.8%
524918
 
5.7%
1023900
 
5.4%
723870
 
5.4%
923630
 
5.4%
823559
 
5.3%
ValueCountFrequency (%)
069875
15.9%
169476
15.8%
266571
15.1%
352729
12.0%
438750
8.8%
524918
 
5.7%
623554
 
5.3%
723870
 
5.4%
823559
 
5.3%
923630
 
5.4%
ValueCountFrequency (%)
1023900
 
5.4%
923630
 
5.4%
823559
 
5.3%
723870
 
5.4%
623554
 
5.3%
524918
 
5.7%
438750
8.8%
352729
12.0%
266571
15.1%
169476
15.8%

Payment Delay
Real number (ℝ)

Zeros 

Distinct31
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean12.965722
Minimum0
Maximum30
Zeros16904
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-12-16T23:24:41.703567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q319
95-th percentile28
Maximum30
Range30
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.2580625
Coefficient of variation (CV)0.63691499
Kurtosis-0.89567824
Mean12.965722
Median Absolute Deviation (MAD)6
Skewness0.26740713
Sum5715705
Variance68.195597
MonotonicityNot monotonic
2025-12-16T23:24:41.831594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1217198
 
3.9%
1117185
 
3.9%
2017175
 
3.9%
1317095
 
3.9%
1417078
 
3.9%
1017051
 
3.9%
717027
 
3.9%
1817027
 
3.9%
317025
 
3.9%
117021
 
3.9%
Other values (21)269950
61.2%
ValueCountFrequency (%)
016904
3.8%
117021
3.9%
216822
3.8%
317025
3.9%
416938
3.8%
516744
3.8%
616954
3.8%
717027
3.9%
816892
3.8%
916869
3.8%
ValueCountFrequency (%)
308590
1.9%
298446
1.9%
288299
1.9%
278178
1.9%
268383
1.9%
258362
1.9%
248325
1.9%
238323
1.9%
228454
1.9%
218670
2.0%

Subscription Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size26.8 MiB
Standard
149128 
Premium
148678 
Basic
143026 

Length

Max length8
Median length7
Mean length6.6893964
Min length5

Characters and Unicode

Total characters2948900
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard
2nd rowBasic
3rd rowBasic
4th rowStandard
5th rowBasic

Common Values

ValueCountFrequency (%)
Standard149128
33.8%
Premium148678
33.7%
Basic143026
32.4%
(Missing)1
 
< 0.1%

Length

2025-12-16T23:24:41.984998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-16T23:24:42.071430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
standard149128
33.8%
premium148678
33.7%
basic143026
32.4%

Most occurring characters

ValueCountFrequency (%)
a441282
15.0%
d298256
10.1%
r297806
10.1%
m297356
10.1%
i291704
9.9%
S149128
 
5.1%
n149128
 
5.1%
t149128
 
5.1%
e148678
 
5.0%
P148678
 
5.0%
Other values (4)577756
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)2948900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a441282
15.0%
d298256
10.1%
r297806
10.1%
m297356
10.1%
i291704
9.9%
S149128
 
5.1%
n149128
 
5.1%
t149128
 
5.1%
e148678
 
5.0%
P148678
 
5.0%
Other values (4)577756
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2948900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a441282
15.0%
d298256
10.1%
r297806
10.1%
m297356
10.1%
i291704
9.9%
S149128
 
5.1%
n149128
 
5.1%
t149128
 
5.1%
e148678
 
5.0%
P148678
 
5.0%
Other values (4)577756
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2948900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a441282
15.0%
d298256
10.1%
r297806
10.1%
m297356
10.1%
i291704
9.9%
S149128
 
5.1%
n149128
 
5.1%
t149128
 
5.1%
e148678
 
5.0%
P148678
 
5.0%
Other values (4)577756
19.6%

Contract Length
Categorical

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size27.1 MiB
Annual
177198 
Quarterly
176530 
Monthly
87104 

Length

Max length9
Median length7
Mean length7.398932
Min length6

Characters and Unicode

Total characters3261686
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnnual
2nd rowMonthly
3rd rowQuarterly
4th rowMonthly
5th rowMonthly

Common Values

ValueCountFrequency (%)
Annual177198
40.2%
Quarterly176530
40.0%
Monthly87104
19.8%
(Missing)1
 
< 0.1%

Length

2025-12-16T23:24:42.186947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-16T23:24:42.277889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
annual177198
40.2%
quarterly176530
40.0%
monthly87104
19.8%

Most occurring characters

ValueCountFrequency (%)
n441500
13.5%
l440832
13.5%
u353728
10.8%
a353728
10.8%
r353060
10.8%
y263634
8.1%
t263634
8.1%
A177198
5.4%
Q176530
 
5.4%
e176530
 
5.4%
Other values (3)261312
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)3261686
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n441500
13.5%
l440832
13.5%
u353728
10.8%
a353728
10.8%
r353060
10.8%
y263634
8.1%
t263634
8.1%
A177198
5.4%
Q176530
 
5.4%
e176530
 
5.4%
Other values (3)261312
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3261686
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n441500
13.5%
l440832
13.5%
u353728
10.8%
a353728
10.8%
r353060
10.8%
y263634
8.1%
t263634
8.1%
A177198
5.4%
Q176530
 
5.4%
e176530
 
5.4%
Other values (3)261312
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3261686
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n441500
13.5%
l440832
13.5%
u353728
10.8%
a353728
10.8%
r353060
10.8%
y263634
8.1%
t263634
8.1%
A177198
5.4%
Q176530
 
5.4%
e176530
 
5.4%
Other values (3)261312
8.0%

Total Spend
Real number (ℝ)

Distinct68363
Distinct (%)15.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean631.61622
Minimum100
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-12-16T23:24:42.395861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile177
Q1480
median661
Q3830
95-th percentile965.8945
Maximum1000
Range900
Interquartile range (IQR)350

Descriptive statistics

Standard deviation240.803
Coefficient of variation (CV)0.38124892
Kurtosis-0.75148884
Mean631.61622
Median Absolute Deviation (MAD)172.55
Skewness-0.45717408
Sum2.7843664 × 108
Variance57986.085
MonotonicityNot monotonic
2025-12-16T23:24:42.538687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
234269
 
0.1%
432267
 
0.1%
703266
 
0.1%
139265
 
0.1%
845265
 
0.1%
581265
 
0.1%
133263
 
0.1%
534262
 
0.1%
613262
 
0.1%
269261
 
0.1%
Other values (68353)438187
99.4%
ValueCountFrequency (%)
100100
< 0.1%
100.021
 
< 0.1%
100.061
 
< 0.1%
100.071
 
< 0.1%
100.081
 
< 0.1%
100.092
 
< 0.1%
100.111
 
< 0.1%
100.122
 
< 0.1%
100.132
 
< 0.1%
100.163
 
< 0.1%
ValueCountFrequency (%)
1000111
< 0.1%
999.995
 
< 0.1%
999.982
 
< 0.1%
999.973
 
< 0.1%
999.967
 
< 0.1%
999.952
 
< 0.1%
999.945
 
< 0.1%
999.934
 
< 0.1%
999.926
 
< 0.1%
999.912
 
< 0.1%

Last Interaction
Real number (ℝ)

Distinct30
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean14.480868
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-12-16T23:24:42.669446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median14
Q322
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.5962077
Coefficient of variation (CV)0.59362517
Kurtosis-1.1537597
Mean14.480868
Median Absolute Deviation (MAD)7
Skewness0.17677405
Sum6383630
Variance73.894787
MonotonicityNot monotonic
2025-12-16T23:24:43.066992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
716914
 
3.8%
1416771
 
3.8%
816762
 
3.8%
1516750
 
3.8%
616746
 
3.8%
116727
 
3.8%
1216722
 
3.8%
316711
 
3.8%
516710
 
3.8%
1016685
 
3.8%
Other values (20)273334
62.0%
ValueCountFrequency (%)
116727
3.8%
216663
3.8%
316711
3.8%
416570
3.8%
516710
3.8%
616746
3.8%
716914
3.8%
816762
3.8%
916532
3.8%
1016685
3.8%
ValueCountFrequency (%)
3012654
2.9%
2912567
2.9%
2812754
2.9%
2712787
2.9%
2612823
2.9%
2512603
2.9%
2412893
2.9%
2312644
2.9%
2212690
2.9%
2112645
2.9%

Churn
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size25.2 MiB
1.0
249999 
0.0
190833 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1322496
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0249999
56.7%
0.0190833
43.3%
(Missing)1
 
< 0.1%

Length

2025-12-16T23:24:43.197012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-16T23:24:43.274874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0249999
56.7%
0.0190833
43.3%

Most occurring characters

ValueCountFrequency (%)
0631665
47.8%
.440832
33.3%
1249999
 
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1322496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0631665
47.8%
.440832
33.3%
1249999
 
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1322496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0631665
47.8%
.440832
33.3%
1249999
 
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1322496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0631665
47.8%
.440832
33.3%
1249999
 
18.9%

Interactions

2025-12-16T23:24:34.921666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:21.115153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:22.906227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:24.992761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:27.020636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:28.961558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:30.987696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:32.923650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:35.150792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:21.333467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:23.151762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:25.217119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:27.240803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:29.207021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:31.211957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:33.137079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:35.572949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:21.551988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:23.361861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:25.433876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:27.452955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:29.457270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:31.477087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:33.343658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:35.888012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:21.767996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:23.583301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:25.666337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:27.735398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:29.677175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:31.717319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:33.561325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:36.140635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:21.988671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:23.837990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:26.083268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:27.965349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:29.951974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:31.951839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:33.790763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:36.432893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:22.195341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:24.072694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:26.309719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:28.201337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:30.256024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:32.182615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:34.027972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:36.706965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:22.423646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:24.307985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:26.547295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:28.453408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:30.527088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:32.439935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:34.271060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:36.977267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:22.650794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:24.767025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:26.785482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:28.691380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:30.748699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:32.695405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-16T23:24:34.682828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-16T23:24:43.352510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeChurnContract LengthCustomerIDGenderLast InteractionPayment DelaySubscription TypeSupport CallsTenureTotal SpendUsage Frequency
Age1.0000.4320.122-0.1630.0660.0260.0510.0060.169-0.010-0.070-0.006
Churn0.4321.0000.4340.9490.1750.1720.4020.0200.6150.0780.4960.059
Contract Length0.1220.4341.0000.2910.0680.0470.1120.0070.1690.0230.1360.016
CustomerID-0.1630.9490.2911.0000.166-0.125-0.2430.013-0.4700.0440.3340.038
Gender0.0660.1750.0680.1661.0000.1560.0630.0030.0970.0110.0770.012
Last Interaction0.0260.1720.047-0.1250.1561.0000.0390.0010.075-0.007-0.052-0.005
Payment Delay0.0510.4020.112-0.2430.0630.0391.0000.0040.146-0.015-0.104-0.013
Subscription Type0.0060.0200.0070.0130.0030.0010.0041.0000.0080.0390.0070.000
Support Calls0.1690.6150.169-0.4700.0970.0750.1460.0081.000-0.027-0.199-0.021
Tenure-0.0100.0780.0230.0440.011-0.007-0.0150.039-0.0271.0000.017-0.027
Total Spend-0.0700.4960.1360.3340.077-0.052-0.1040.007-0.1990.0171.0000.017
Usage Frequency-0.0060.0590.0160.0380.012-0.005-0.0130.000-0.021-0.0270.0171.000

Missing values

2025-12-16T23:24:37.202641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-16T23:24:37.743872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-16T23:24:39.056857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CustomerIDAgeGenderTenureUsage FrequencySupport CallsPayment DelaySubscription TypeContract LengthTotal SpendLast InteractionChurn
02.030.0Female39.014.05.018.0StandardAnnual932.017.01.0
13.065.0Female49.01.010.08.0BasicMonthly557.06.01.0
24.055.0Female14.04.06.018.0BasicQuarterly185.03.01.0
35.058.0Male38.021.07.07.0StandardMonthly396.029.01.0
46.023.0Male32.020.05.08.0BasicMonthly617.020.01.0
58.051.0Male33.025.09.026.0PremiumAnnual129.08.01.0
69.058.0Female49.012.03.016.0StandardQuarterly821.024.01.0
710.055.0Female37.08.04.015.0PremiumAnnual445.030.01.0
811.039.0Male12.05.07.04.0StandardQuarterly969.013.01.0
912.064.0Female3.025.02.011.0StandardQuarterly415.029.01.0
CustomerIDAgeGenderTenureUsage FrequencySupport CallsPayment DelaySubscription TypeContract LengthTotal SpendLast InteractionChurn
440823449990.048.0Male11.027.01.018.0StandardAnnual618.285.00.0
440824449991.041.0Male46.025.03.02.0StandardQuarterly619.7915.00.0
440825449992.041.0Male27.020.02.012.0StandardQuarterly634.1727.00.0
440826449993.049.0Male37.023.04.016.0StandardAnnual666.6530.00.0
440827449994.045.0Male6.025.02.015.0BasicAnnual837.002.00.0
440828449995.042.0Male54.015.01.03.0PremiumAnnual716.388.00.0
440829449996.025.0Female8.013.01.020.0PremiumAnnual745.382.00.0
440830449997.026.0Male35.027.01.05.0StandardQuarterly977.319.00.0
440831449998.028.0Male55.014.02.00.0StandardQuarterly602.552.00.0
440832449999.031.0Male48.020.01.014.0PremiumQuarterly567.7721.00.0